Method and an apparatus for predicting intake manifold pressure of an internal-combustion engine

ABSTRACT

A method and an apparatus for predicting intake manifold pressure are presented, to compensate for a large lag or a large time delay without producing an overshot or discontinuous behaviors of a predicted value. The method comprises the step of obtaining a difference of values of a variable to be predicted and a difference of values of another variable ahead of the variable to be predicted. The method further comprises the step of filtering the differences with adaptive filters. The method further comprises the step of obtaining a predicted difference of values of the variable to be predicted, through algorithm of estimation with fuzzy reasoning. The method further comprises the step of adding the predicted difference of values of the variable to be predicted, to a current value of the variable to be predicted, to obtain a predicted value of the variable to be predicted.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and an apparatus forpredicting an intake manifold pressure of an internal-combustion engine.In particular, the present invention relates to a method and a fuzzyestimator for predicting an intake manifold pressure, using fuzzyalgorithm.

2. Description of the Related Art

Fuel injection control is carried out for efficient combustion ininternal-combustion engines. FIG. 1 shows an intake portion of aninternal-combustion engine, to which the present invention is applied. Adrawn air is supplied to a cylinder through a throttle 1. Throttleopening is controlled at a desired value. An intake manifold pressure ismeasured with a sensor 2. For appropriate fuel injection control, anamount of air to be drawn into the cylinder must be estimated andtherefore a value of intake manifold pressure must be predicted.

Conventionally, such an intake manifold pressure predicting apparatus asdescribed below was disclosed, for example in Publication of JapaneseUnexamined Patent Publication (KOKAI) No. 2-42160. The apparatuspredicts an intake manifold pressure at the present time based onoperating states of the internal-combustion engine and predicts intakemanifold pressure at a time of prediction, a certain period ahead, basedon the predicted intake manifold pressure and a detected value of intakemanifold pressure.

However, the above-mentioned predicting apparatus was not able to make aprediction with sufficient accuracy over a wide range of number ofrevolutions of the internal-combustion engine. The reason is that theapparatus corrects a predicted intake manifold pressure during a periodwhile an intake valve is transiently closed, evenly based on an error inintake manifold pressure ΔP and independently of operating states of theinternal-combustion engine.

Japanese Patent No. 2886771 discloses an intake manifold pressurepredicting apparatus which makes a prediction considering operatingstates of the internal-combustion engine to solve the above-mentionedproblem, and enables a high accuracy control even in the case of lowernumber of revolutions and higher intake manifold pressures.

In the conventional method mentioned above, a predicted value of intakemanifold pressure (hereinafter referred to as HATPB) is calculated basedon a difference (hereinafter referred to as ΔPB) between successivevalues of intake manifold pressure (hereinafter referred to as PB) and adifference (hereinafter referred to as ΔTH) between successive values ofthrottle opening. Further, HATPB is used for fuel injection control andretrieval of parameters for fuel adhesion correction. Now, ΔTH and ΔPBare represented as below provided that k is a point in control timesynchronized with intake stroke (TDC).ΔTH(k)=TH(k)+TH(k−1)  (1)ΔPB(k)=PB(k)+PB(k−1)  (2)

On the other hand, between the detecting portion of a PB sensor and anintake manifold, on the intake manifold side or in the PB sensor, alabyrinth mechanism or the like has recently been provided to preventwater from entering there. Accordingly, a time lag and a time delaybetween an actual value of pressure and an output of the PB sensor, havebecome larger.

There have been attempts to use conventional prediction algorithms tocompensate the time delay. However, the algorithms have caused anovershoot of HATPB against an actual PB, as shown in FIG. 2 or adiscontinuous behavior of HATPB. The reason for the overshoot is thatthe conventional prediction algorithms make up for insufficient accuracyof a predicted value of PB through feedback of an error between apredicted value of PB a certain time in advance and the current value ofPB. Further, the reason for the discontinuous behavior is that theconventional prediction algorithms use one of predicted valuescalculated respectively based on ΔPB and ΔTH, by switching according tocertain conditions. Such behaviors of the conventional predictionalgorithms have such an influence on fuel injection control as to causea problem that variation in variables during transient operationsbecomes larger to increase an amount of emissions of offensive exhaustelements.

Accordingly, there has been a need for a new prediction algorithm for PBto compensate for a larger lag and a larger time delay without producingan overshoot or a discontinuous behavior of HATPB.

SUMMARY OF THE INVENTION

A method for predicting intake manifold pressure according to an aspectof the present invention, comprises the step of obtaining a differenceof values of intake manifold pressure and a difference of values ofthrottle opening. The method further comprises the step of obtaining apredicted difference of values of intake manifold pressure, throughalgorithm of estimation with fuzzy reasoning. The method furthercomprises the step of adding the predicted difference of values ofintake manifold pressure, to a value of intake manifold pressure, toobtain a predicted value of intake manifold pressure. The algorithm ofestimation with fuzzy reasoning, includes fuzzy rules determined basedon an amount of a difference of values of intake manifold pressure andan amount of a difference of values of throttle opening.

An apparatus for predicting intake manifold pressure, according toanother aspect of the present invention, comprises a device forobtaining a difference of values of intake manifold pressure, a devicefor obtaining a difference of values of throttle opening and a fuzzyestimator. The fuzzy estimator receives as inputs the difference ofvalues of intake manifold pressure and the difference of values ofthrottle opening and obtains and outputs a predicted difference ofvalues of intake manifold pressure, through algorithm of estimation withfuzzy reasoning. The algorithm of estimation with fuzzy reasoningincludes fuzzy rules determined based on an amount of a difference ofvalues of intake manifold pressure and an amount of a difference ofvalues of throttle opening.

A computer-readable medium, according to another aspect of the presentinvention, has a program stored therein, which is made to perform thestep of obtaining a difference of values of intake manifold pressure anda difference of values of throttle opening. The program is further madeto perform the step of obtaining a predicted difference of values ofintake manifold pressure, through algorithm of estimation with fuzzyreasoning. The program is further made to perform the step of adding thepredicted difference of values of intake manifold pressure, to a valueof intake manifold pressure, to obtain a predicted value of intakemanifold pressure. The algorithm of estimation with fuzzy reasoning,includes fuzzy rules determined based on an amount of a difference ofvalues of intake manifold pressure and an amount of a difference ofvalues of throttle opening.

Thus, in the aspects of the present invention mentioned above, apredicted value of an output of the PB sensor is calculated throughalgorithm of estimation with fuzzy reasoning, based an on output of thePB sensor and TH opening. Then, an amount of fuel injection into theinternal combustion engine is determined based on the predicted value.In particular, use of fuzzy rules based on an amount of ΔPB and that ofΔTH allows a control effectively containing information on a change inTH which is ahead of a change in PB. According to the aspects of thepresent invention mentioned above, even when there occurs a large timedelay or a large lag between an actual value of intake manifold pressure(negative) and an output of the PB sensor, predicted values will not bediscontinuous as in conventional methods and predicted values which arecontinuous can be calculated with higher accuracy. Accordingly, anair-fuel ratio of the internal combustion engine will not show adiscontinuous behavior. Further, an overshoot of the predicted valueagainst the actual value of intake manifold pressure, can bedramatically reduced compared with conventional methods.

According to an embodiment of the present invention, a difference ofvalues of intake manifold pressure is classified based on its amountinto positive one, that of zero or negative one and a difference ofvalues of throttle opening is classified based on its amount intopositive one, that of zero or negative one. Further, fuzzy rules areprovided respectively for areas determined by the two kinds ofclassifications.

Accordingly, operations for estimation through fuzzy reasoning will notbecome complicated and can be carried out simply and efficiently.

According to another embodiment of the present invention, a second orderdifference of values of intake manifold pressure, is further obtained.Then, fuzzy rules are determined based on an amount of a difference ofvalues of intake manifold pressure, an amount of a difference of valuesof throttle opening and an amount of a second order difference of valuesof intake manifold pressure.

Accordingly, a control effectively using information on behaviors of ΔPBin the future, contained in ΔΔPB (a second order difference of values ofintake manifold pressure), can be carried out.

According to another embodiment of the present invention, a second orderdifference of values of intake manifold pressure is classified based onits amount into positive one, that of zero or negative one. Then, fuzzyrules are provided respectively for areas determined by three kinds ofclassifications based on an amount of a difference of values of intakemanifold pressure, an amount of a difference of values of throttleopening and an amount of a second order difference of values of intakemanifold pressure.

Accordingly, operations for estimation through fuzzy reasoning will notbecome complicated and can be carried out simply and efficiently.

According to another embodiment of the present invention, a valueobtained by delaying a throttle opening value by a time delay, is usedas a throttle opening value.

Accordingly, even if a volume of the intake manifold is so large thatthere occurs a time delay between a change in throttle opening and achange in actual PB, information on TH can be used for prediction andpredicted values which do not shown discontinuous behaviors, can becalculated with higher accuracy.

According to another embodiment of the present invention, a relationshipbetween a throttle opening value and a desired value of throttle openingis modeled using a time delay element and a lag system and a valueestimated through the model and the desired value is used as a throttleopening value. That is, provided that an estimated value of throttleopening and a desired value of throttle opening at point k in time, arerespectively represented as THHAT(k) and THCMD(k), a value correspondingto a time delay is represented as ddly and a constant is represented asKdly, an estimated value of throttle opening THHAT(k) is obtained by thefollowing equation for use.THHAT(k)=Kdly×THHAT(k)+(1−Kdly)×THCMD(k−ddly)

Accordingly, even when an electronically controlled throttle involving atime delay before reading of TH, is used, a predicted value can becalculated with high accuracy.

According to another embodiment of the present invention, the membershipfunction for the consequent part of the algorithm of estimation withfuzzy reasoning, is a bar-shaped singleton function.

Accordingly, operations for estimation through fuzzy reasoning will notbecome complicated and can be carried out simply and efficiently. Aprocessor proof against such service conditions for vehicles ascryogenic temperatures, high temperatures, high humidity and vibration,can hardly be provided with such high computing ability as is able tocarry out mini-max gravity method of the algorithm of estimation withfuzzy reasoning. However, use of a bar-shaped singleton function as themembership function for the consequent part, enables the processor tocarry out mini-max gravity method and to calculate a predicted valuewith high accuracy.

According to another embodiment of the present invention, inputs aresubjected to filtering.

Accordingly, use of filtered data as input data to the algorithm ofestimation with fuzzy reasoning, prevents a predicted value fromoscillating even when noises are mixed.

According to another embodiment of the present invention, the filteringis carried out with an adaptive filter.

Accordingly, noises can be eliminated from the data to a sufficientdegree while maintaining a phase delay of the data minimum so that thedata can be used for predicting operations.

A method for obtaining a predicted value of a variable, according to anaspect of the present invention, comprises the step of obtaining adifference of values of a variable to be predicted and a difference ofvalues of another variable ahead of the variable to be predicted. Themethod further comprises the step of filtering the differences withadaptive filters. The method further comprises the step of obtaining apredicted difference of values of the variable to be predicted, throughalgorithm of estimation with fuzzy reasoning. The method furthercomprises the step of adding the predicted difference of values of thevariable to be predicted, to a current value of the variable to bepredicted, to obtain a predicted value of the variable to be predicted.The algorithm of estimation with fuzzy reasoning includes fuzzy rulesdetermined based on an amount of a difference of values of the variableto be predicted and an amount of a difference of values of the variableahead of the variable to be predicted.

A predicting apparatus, according to an aspect of the present invention,comprises filters for filtering inputs and a fuzzy estimator. The fuzzyestimator receives as inputs a difference of values of a variable to bepredicted and a difference of values of another variable ahead of thevariable to be predicted and obtains and outputs a predicted differenceof values of the variable to be predicted, through algorithm ofestimation with fuzzy reasoning. The algorithm of estimation with fuzzyreasoning includes fuzzy rules determined based on an amount of adifference of values of the variable to be predicted and an amount of adifference of values of the variable ahead of the variable to bepredicted.

A computer-readable medium, according to an aspect of the presentinvention, has a program stored therein, which is made to perform thestep of obtaining a difference of values of the variable to be predictedand a difference of values of another variable ahead of the variable tobe predicted. The program is further made to perform the step offiltering the differences with adaptive filters. The program is furthermade to perform the step of obtaining a predicted difference of valuesof the variable to be predicted, through algorithm of estimation withfuzzy reasoning. The program is further made to perform the step ofadding the predicted difference of values of the variable to bepredicted, to a current value of the variable to be predicted, to obtaina predicted value of the variable to be predicted. The algorithmincludes fuzzy rules determined based on an amount of a difference ofvalues of the variable to be predicted and an amount of a difference ofvalues of the variable ahead of the variable to be predicted.

Thus, according to the aspects mentioned above, use of fuzzy rules basedon an amount of a difference of the variable to be predicted and that ofthe variable ahead of the variable to be predicted, allows a controleffectively containing information on a change in the variable ahead ofthe variable to be predicted. Further, use of adaptive filters allowsnoises to be eliminated from the data to a sufficient degree whilemaintaining a phase delay of the data minimum so that the data can beused for predicting operations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an intake portion of an internal-combustion engine.

FIG. 2 shows behaviors of a value predicted by conventional algorithmsfor predicting intake manifold pressure.

FIG. 3 shows relationships among PB, TH, ΔPB, ΔTH and ΔFZPB.

FIG. 4 shows membership functions for the antecedent part according toan embodiment of the present invention.

FIG. 5 shows membership function for the consequent part according to anembodiment of the present invention.

FIG. 6 shows fuzzy rules used in an embodiment of the present invention.

FIG. 7 shows a state to which Rule 1 is applied.

FIG. 8 shows a state to which Rule 2 is applied.

FIG. 9 shows a state to which Rule 3 is applied.

FIG. 10 shows a state to which Rule 4 is applied.

FIG. 11 shows a state to which Rule 5 is applied.

FIG. 12 shows a state to which Rule 6 is applied.

FIG. 13 shows a state to which Rule 7 is applied.

FIG. 14 shows a state to which Rule 8 is applied.

FIG. 15 shows a state to which Rule 9 is applied.

FIG. 16 shows a method by which the degree of fulfillment for Rule 6 isobtained based on the membership functions for the antecedent part.

FIG. 17 shows a method by which a weight of Rule 6 is obtained based onthe membership function for the consequent part.

FIG. 18 shows the result of estimation of FZPB using the fussy inferencealgorithm according to an embodiment of the present invention.

FIG. 19 shows the membership functions of the antecedent part accordingto another embodiment of the present invention.

FIG. 20 shows fuzzy rules according to another embodiment of the presentinvention.

FIG. 21 shows the membership function of the consequent part accordingto another embodiment of the present invention.

FIG. 22 shows a state to which Rule 10 is applied.

FIG. 23 shows a state to which Rule 11 is applied.

FIG. 24 shows a state to which Rule 6 is applied according to anotherembodiment of the present invention.

FIG. 25 shows a state to which Rule 9 is applied according to anotherembodiment of the present invention.

FIG. 26 shows the result of estimation of FZPB using the fussy inferencealgorithm according to another embodiment of the present invention.

FIG. 27 shows a fuzzy estimator according to an embodiment of thepresent invention.

FIG. 28 shows a fuzzy estimator provided with adaptive filters accordingto an embodiment of the present invention.

FIG. 29 shows behaviors of FZPB with and without the use of adaptivefilters.

FIG. 30 shows a state in which an electronically controlled throttle isused.

FIG. 31 shows a fuzzy estimator for the use with an electronicallycontrolled throttle.

FIG. 32 shows the result of prediction of a fuzzy estimator for the usewith an electronically controlled throttle.

FIG. 33 shows a fuzzy estimator for the use with a large-volume intakemanifold.

FIG. 34 shows a procedure of the embodiment of the present invention inwhich adaptive filters are used.

FIG. 35 shows an example of an electronic control unit used inembodiments of the present invention.

DETAILED DESCRIPTION OF THE PREFFERRED EMBODIMENTS

In the present invention, a predicted amount of change in PB(hereinafter referred to as ΔFZPB) is obtained through estimation withfuzzy reasoning, using fuzzy algorithm including fuzzy rules definedbased on an amount of ΔPBand that of ΔTH. Then, a predicted value(hereinafter referred to as FZPB) through estimation with fuzzyreasoning is calculated by the following equation.FZPB(k)=PB(k)+ΔFZPB(k)  (3)

That is, ΔFZPB(k) is added to the current PB sample value PB(k) toobtain FZPB(k). “k” indicates a point in control time synchronized withintake stroke (TDC). FIG. 3 shows relationships among PB, TH, ΔPB, ΔTHand ΔFZPB.

FIG. 6 shows fuzzy rules used in an embodiment of the present invention.ΔPB is classified based on its amount into positive one, that of zero ornegative one. ΔTH is classified based on its amount into positive one,that of zero or negative one. Fuzzy rules are provided respectively for9 areas determined by the two kinds of classifications. It should benoted here that a change in TH is ahead of a change in PB and thereforecontains information on behaviors of PB in the future. Accordingly, useof fuzzy rules based on an amount of ΔPB and that of ΔTH allows acontrol effectively containing information on a change in TH which isahead of a change in PB.

FIGS. 4 and 5 respectively show membership functions for the antecedentpart and that for the consequent part. The membership functions for theantecedent part for ΔPB and ΔTH are set trapezoidal for positive (P) andnegative (N) and set triangular for zero (Z). As the membership functionfor the consequent part, a bar-shape singleton function is used forsimple operations of estimation with fuzzy reasoning.

Now each rule (Rule 1 to Rule 9 in FIG. 6) in each area will bedescribed with reference to drawings.

FIG. 7 shows a state in which both ΔPB and ΔTH are negative. Rule 1 isapplied to the state. Since both ΔPB and ΔTH are negative, ΔFZPB of themembership function for the consequent part is also set negative.

FIG. 8 shows a state in which ΔPB is negative and ΔTH is zero. Rule 2 isapplied to the state. Since ΔPB is negative while ΔTH which is ahead ofΔPB is zero, ΔFZPB of the membership function for the consequent part isset negative.

FIG. 9 shows a state in which ΔPB is negative and ΔTH is positive. Rule3 is applied to the state. The state corresponds to the case in whichduring operation of engine brake number of revolutions of the engineincreases more rapidly than an amount of air passing through thethrottle increases due to increase in TH. ΔFZPB of the membershipfunction for the consequent part is set zero.

FIG. 10 shows a state in which ΔPB is zero and ΔTH is negative. Rule 4is applied to the state. Since ΔTH which is ahead of ΔPB is negative,ΔFZPB of the membership function for the consequent part is setnegative.

FIG. 11 shows a state in which both ΔPB and ΔTH are zero. Rule 5 isapplied to the state. Since both ΔPB and ΔTH are zero, ΔFZPB of themembership function for the consequent part is also set zero.

FIG. 12 shows a state in which ΔPB is zero and ΔTH is positive. Rule 6is applied to the state. Since ΔTH which is ahead of ΔPB is positive,ΔFZPB of the membership function for the consequent part is set positive(P₆).

FIG. 13 shows a state in which ΔPB is positive and ΔTH is negative. Rule7 is applied to the state. The state corresponds to the case in whichdue to an external force number of revolutions of the engine decreasesmore significantly than it would decrease due to decrease in TH. ΔFZPBof the membership function for the consequent is set zero.

FIG. 14 shows a state in which ΔPB is positive and ΔTH is zero. Rule 8is applied to the state. ΔFZPB of the membership function for theconsequent is set positive (P₈). Since ΔTH which is ahead of ΔPB iszero, P₈ is set smaller than P₆ in Rule 6.

FIG. 15 shows a state in which both ΔPB and ΔTH are positive. Rule 9 isapplied to the state. ΔFZPB of the membership function for theconsequent is set positive (P₁₀). Since ΔPB has already been positive,P₁₀ is set smaller than P₆ in Rule 6 for the state in which ΔPB is zero.

Final ΔFZPB is calculated based on mini-max gravity method using themembership functions and fuzzy rules mentioned above. The method will bedescribed in detail for the case to which Rule 6 is applied, as anexample.

Now mini-max selection will be described. Current sample values of ΔPBand ΔTH are represented as ΔPB(k) and ΔTH(k). Degrees of fulfillment ofthese values for Rule 6 will be obtained. As shown in FIGS. 6 and 12,Rule 6 is applied to an area in which ΔPB is zero and ΔTH is positive.Thus the fulfillment of ΔPB for the membership function (zero) of theantecedent part is mΔPB(6) as shown in FIG. 16( a). Further, thefulfillment of ΔTH for the membership function (positive) of theantecedent part is mΔTH(6) as shown in FIG. 16( b). In mini-maxselection, the smallest degree of fulfillment among the degrees offulfillment for the antecedent part, is selected as the degree offulfillment m(i) for rule i. The following relationship is established.mΔPB (6)<mΔTH (6)  (4)

Accordingly, for the degree of fulfillment for Rule 6, mΔPB(6) isselected.m (6)=mΔPB (6)  (5)

Further, a position of the membership function of the consequent partfor Rule 6 is pP6 and a reference weight (length of bar) of that is wP6as shown in FIG. 17. Accordingly, weight of Rule 6 in estimation ofΔFZPB, that is weight w(6) at position pP, will be given as below.w(6)=m(6)×wP6  (6)

Then, a weight w(i) is obtained for each rule i in a similar way. Allthe weights thus obtained are used for estimation and therefore this is“max” selection.

Estimation of ΔFZPB, that is defuzzification of a fuzzy output will becarried out using a gravity method shown in the following equation,based on weights for the rules.

$\begin{matrix}{{\Delta\mspace{11mu}{FZPB}\mspace{11mu}(k)} = \frac{\sum\limits_{i = 1}^{9}{{w(i)} \times {wPi} \times {pPi}}}{\sum\limits_{i = 1}^{9}{{w(i)} \times {wPi}}}} & (7)\end{matrix}$

Thus, equation (3) is represented as below and ΔFZPB(k) can becalculated through it.

$\begin{matrix}{{{\Delta\mspace{11mu}{FZPB}\mspace{11mu}(k)}=={{PB}(k)}} = \frac{\sum\limits_{i = 1}^{9}{{w(i)} \times {wPi} \times {pPi}}}{\sum\limits_{i = 1}^{9}{{w(i)} \times {wPi}}}} & (8)\end{matrix}$

FIG. 18 shows the result of estimation of FZPB using the fussy inferencealgorithm mentioned above. In FIG. 18 FZPB is closely analogous to thedesirable predicted value, even though there exists some overshootagainst the desirable predicted value. Thus, accuracy of estimation hassignificantly increased compared with that of the conventional algorithmshown in FIG. 2.

Now another embodiment of the present invention will be described below.The prediction with fuzzy reasoning in the embodiment mentioned above,produces some overshoot as shown in FIG. 18. Such an overshoot tends tooccur particularly under hard acceleration or snap. In order toeliminate such an overshoot, a change in ΔPB, that is second orderdifference of PB (hereinafter referred to as ΔΔPB) should be noted. ΔΔPBis defined by the following equation where “k” indicates a point incontrol time synchronized with intake stroke (TDC).ΔΔPB(K)=ΔPB(k) 31 ΔPB(K−1)  (9)

Further, fuzzy rules will be defined based on an amount of ΔPB, that ofΔTH and that of ΔΔPB. Thus, the three kinds of classifications due to anamount of ΔPB, that of ΔTH and that of ΔΔPB, generate 27 areas, for eachof which a fuzzy rule is provided. FIG. 20 shows fuzzy rules accordingto the present embodiment. Fuzzy rules shown in FIG. 20 are positionedin three dimensions, while those shown in FIG. 6 are positioned in twodimensions. It should be noted here that ΔΔPB contains information onbehaviors of ΔPB in the future. Accordingly, use of fuzzy rules based onan amount of ΔΔPB allows a control containing information on theinformation on behaviors of ΔPB in the future. FIG. 19 shows themembership functions of the antecedent part according to the presentembodiment.

Compared with fuzzy rules shown in FIG. 6, in FIG. 20 Rule 8 and Rule 9alone differ respectively when ΔΔPB is positive and when ΔΔPB isnegative. The other rules remain unchanged independently of ΔΔPB.

Now new rules (Rules 10 and 11 in FIG. 20) will be described withreference to drawings.

FIG. 22 shows a state in which ΔPB, ΔTH and ΔΔPB are positive. Rule 10is applied to the state. Since ΔPB, ΔTH and ΔΔPB are positive, ΔFZPB ofthe membership function for the consequent is set to the largestpositive value (P₁₀).

FIG. 23 shows a state in which ΔPB is positive, ΔTH is zero and ΔΔPB isnegative. Rule 11 is applied to the state. ΔFZPB of the membershipfunction for the consequent part is set zero.

FIG. 24 shows a state in which ΔPB is zero and ΔTH is positive,according to the present embodiment. Rule 6 is applied to the state. Asmentioned above, Rule 10 is applied to a state in which ΔPB, ΔTH andΔΔPB are positive. Accordingly, an area to which Rule 6 is applied isreduced, compared with the case in which ΔΔPB is not used.

FIG. 25 shows a state in which both ΔPB and ΔTH are positive, accordingto the present embodiment. Rule 9 is applied to the state. As mentionedabove, Rule 10 is applied to a state in which ΔPB, ΔTH and ΔΔPB arepositive. Accordingly, an area to which Rule 9 is applied is reduced,compared with the case in which ΔΔPB is not used.

FIG. 21 shows the membership function of the consequent part accordingto the present embodiment. As mentioned above, P₁₀ is the largestpositive value. Compared with the membership function of the consequentpart shown in FIG. 5, pP6 (position of P₆) and pP9 (position of P₉) hasapproached zero. The reason is that areas to which Rule 6 and Rule 9 areapplied, have been reduced.

FIG. 26 shows the result of estimation of FZPB using the fussy inferencealgorithm according to the present embodiment. In FIG. 26 such anovershoot as shown in FIG. 18 has been eliminated.

In the present embodiment when a time delay to be compensated is small,P₆ and P₉ having approached zero, can be set zero for the sake ofsimplifying data setting. Further, the state to which Rule 8 is appliedand in which ΔΔPB is positive, rarely occurs. Accordingly, this rule maybe eliminated, or the consequent part may be set zero to invalidate itscontributions to the prediction.

Now embodiments in which an input is subjected to filtering, will bedescribed. The present invention employs algorithm of estimation withfuzzy reasoning, using ΔPB, ΔTH and ΔΔPB as inputs. Accordingly, ifnoises are mixed into TH and/or PB, the values of difference and secondorder difference will be oscillating or have spikes. As a result, FZPBestimated through estimation with fuzzy reasoning will also beoscillating or have spikes at times.

In the present embodiment, ΔPB, ΔTH and ΔΔPB are subjected to filteringand then input to a fuzzy estimator as shown FIG. 28. On the contrary,in FIG. 27 ΔPB, ΔTH and ΔΔPB are input to a fuzzy estimator. In FIG. 27devices for obtaining difference are represented with reference numerals2711 to 2713 and a fuzzy estimator is represented with reference numeral2701. In FIG. 28 devices for obtaining difference are represented withreference numerals 2811 to 2813, filters are represented with referencenumerals 2821 to 2823 and a fuzzy estimator is represented withreference numeral 2801.

As the filters for ΔPB, ΔTH and ΔΔPB, such an adaptive filter asrepresented in the following equations, is employed. This type offilters prevents FZPB from oscillating or having spikes due to noises asshown in FIG. 29.Xf(k)=X _(—) f(k−1)+KP(k)·ide(k)  (10)KP(k)=P(k−1)/(1+P(k−1))  (11)ide(k)=X ₁₃ f(k−1)−X(k)  (12)P(k+1)=(1/λ ₁)[1−λ₂ ·P(k)/(λ₁+λ₂ ·P(k))]  (13)X_f represents adaptive filter values for ΔPB, ΔTH and ΔΔPB, while Xrepresents sample values of ΔPB, ΔTH and ΔΔPB. λ₁ and λ₂ representweighting parameters.

Now, another embodiment will be described, in which TH is determined inconsideration of time delay, based on a desired value of throttleopening. Recently an electronically controlled throttle has been widelyemployed to fill the need for coordinated control with the transmissionfor better fuel economy and for control for stabilizing steering. Anelectronically controlled throttle is often driven by a driver separatefrom the electronic control unit. The driver is connected to theelectronic control unit via a network on the vehicle (CAN or the like).

Accordingly, a period (10 milliseconds) of communication between theelectronic control unit and the driver, causes a time delay betweenthrottle opening command THCMD calculated by the electronic control unitand an actual throttle opening THACT of the electronically controlledthrottle. That is, a time delay occurs before THCMD exerts an influenceupon TH. Further, another time delay occurs before THACT exerts aninfluence upon TH observed by the electronic control unit via the CAN orthe like. FIG. 30 shows a state in which an electronically controlledthrottle is used. In FIG. 30 THCMD represents a command (reference) ofthrottle opening, THACT represents an actual throttle opening and THrepresents an (observed) value of throttle opening, as mentioned above.

Thus, a change in TH observed by the electronic control unit lags behinda change in PB and therefore ΔTH cannot be used to predict PB as in thealgorithm of estimation with fuzzy reasoning mentioned above.

Accordingly, in the present embodiment THHAT is estimated based on THCMDin consideration of a time delay due to communication and a delay inresponse of the electronically controlled throttle, as described below.A difference in THHAT, that is Δ□□HAT is used instead of ΔTH.THHAT(k)=Kdly×THHAT(k)+(1−Kdly)×THCMD(k−ddly)  (14)ΔTHHAT(k)=THHAT(k)−THHAT(k−1)  (15)

ddly represents a value corresponding to a time delay and Kdlyrepresents a constant.

FIG. 31 shows a system configuration including devices and FIG. 32 showsthe result of prediction, according to the present embodiment. In FIG.31 devices for obtaining difference are represented with referencenumerals 3111 to 3113, filters are represented with reference numerals3121 to 3123, a module for carrying out operation represented byEquation (14) is represented with reference numeral 3131 and a fuzzyestimator is represented with reference numeral 3101.

Now, another embodiment in which TH is delayed by a time delay, will bedescribed. Recently a volume of the intake manifold of the engine inmany cars has been made very large to fill the need for a larger torqueat lower speeds. In these cases a change in an actual pressure at theintake manifold, lags a time delay dth behind a change in TH. Under thesituation a change in TH is too ahead of a change in an actual pressureat the intake manifold to use TH for operations for prediction of anactual pressure at the intake manifold. In order to solve this problem,TH is delayed by a time delay dth before being used in operations forprediction. In FIG. 33 devices for obtaining difference are representedwith reference numerals 3311 to 3313, a time delay module is representedwith reference numeral 3341 and a fuzzy estimator is represented withreference numeral 3301.

A procedure of the embodiment of the present invention in which adaptivefilters are used, will be described with reference to a flowchart shownin FIG. 34. In step S10 a PB sensor is checked for whether it is activeor not. If the PB sensor is not active, the process proceeds with stepS80, in which a substitute value is given for a sample value of PB.Further, in step S 90 the substitute value as the sample value is set toFZPB and the process is terminated. If the PB sensor is active, theprocess proceeds with step S 20, in which the PB sensor is checked forwhether it normally functions or not. If it does not normally function,the process proceeds with step S 80. If it normally functions, theprocess proceeds with step S 30, in which an estimated value THHAT of THis calculated (Equation (14)). THHAT is used instead of TH for the caseof an electronically controlled throttle. Then, the process proceedswith step S40, in which ΔPB, ΔΔPB and ΔTHHAT are calculated (Equations(2), (7) and (14)). Further, the process proceeds with step S50, inwhich operations for adaptive filters are carried out (Equations (10) to(13)). Then, in step S60 ΔFZPB is estimated. In step S70 ΔFZPB is addedto a sample value of PB to obtain FZPB and the process is terminated.

An example of an electronic control unit used in embodiments of thepresent invention, will be described with reference to FIG. 35. Theelectronic control unit includes a CPU 3501, a ROM 3511, a flash memory3512, a RAM 3513, an I/O unit 3514 and a communication controller 3515for a network on the vehicle. The above devices are connected with oneanother via a bus 3520.

Algorithm for predicting intake manifold pressure according to thepresent invention, may be stored as a program in the ROM 3511 or theflash memory 3512. Some part of the algorithm, for example fuzzy rules,may be stored in the flash memory 3512, while the other part may bestored in the ROM 3511. Alternatively, the algorithm may be stored inanother type of memory not shown in the drawing.

1. A method for predicting intake manifold pressure, said methodcomprising the steps of: obtaining a difference of values of intakemanifold pressure and a difference of values of throttle opening;obtaining a predicted difference of values of intake manifold pressureby estimating with fuzzy reasoning, including fuzzy rules determinedbased on an amount of the difference of values of intake manifoldpressure and an amount of the difference of values of throttle opening;and adding the predicted difference of values of intake manifoldpressure, to a value of intake manifold pressure, to obtain a predictedvalue of intake manifold pressure.
 2. A method for predicting intakemanifold pressure according to claim 1, wherein the difference of valuesof intake manifold pressure is classified according to a firstclassification based on its amount into positive one, that of zero ornegative one and the difference of values of throttle opening isclassified according to a second classification based on its amount intopositive one, that of zero or negative one and fuzzy rules are providedrespectively for areas determined by the first classification and thesecond classification.
 3. A method for predicting intake manifoldpressure according to claim 1, wherein in the step of obtaining adifference, a second order difference of values of intake manifoldpressure, is further obtained and in the step of obtaining the predicteddifference, fuzzy rules are determined based on an amount of thedifference of values of intake manifold pressure, an amount of thedifference of values of throttle opening and an amount of the secondorder difference of values of intake manifold pressure.
 4. A method forpredicting intake manifold pressure according to claim 3, wherein asecond order difference of values of intake manifold pressure isclassified based on its amount into positive one, that of zero ornegative one and fuzzy rules are provided respectively for areasdetermined by three kinds of classifications based on an amount of thedifference of values of intake manifold pressure, an amount of thedifference of values of throttle opening and an amount of the secondorder difference of values of intake manifold pressure.
 5. A method forpredicting intake manifold pressure according to claim 1, wherein avalue obtained by delaying an input throttle opening value by a timedelay is used as an output throttle opening value.
 6. A method forpredicting intake manifold pressure according to claim 1, wherein arelationship between an input throttle opening value and a desired valueof throttle opening is modeled using a time delay element and a lagsystem and a value estimated through the model and the desired value isused as an output throttle opening value.
 7. A method for predictingintake manifold pressure according to claim 1, wherein a membershipfunction for a consequent part of the estimating with fuzzy reasoning isa bar-shaped singleton function.
 8. A method for predicting intakemanifold pressure according to claim 1, further comprising the step offiltering between the step of obtaining differences and the step ofobtaining the predicted difference.
 9. A method for predicting intakemanifold pressure according to claim 8, wherein the filtering is carriedout with an adaptive filter.
 10. An apparatus for predicting intakemanifold pressure, said apparatus comprising: a first device forobtaining a difference of values of intake manifold pressure; a seconddevice for obtaining a difference of values of throttle opening; and afuzzy estimator receiving as inputs the difference of values of intakemanifold pressure and the difference of values of throttle opening andobtaining and outputting a predicted difference of values of intakemanifold pressure by estimating with fuzzy reasoning, including fuzzyrules determined based on an amount of the difference of values ofintake manifold pressure and an amount of the difference of values ofthrottle opening.
 11. An apparatus for predicting intake manifoldpressure according to claim 10, wherein in the fuzzy estimator thedifference of values of intake manifold pressure is classified accordingto a first classification based on its amount into positive one, that ofzero or negative one and the difference of values of throttle opening isclassified according to a second classification based on its amount intopositive one, that of zero or negative one and fuzzy rules are providedrespectively for areas determined by the first classification and thesecond classification.
 12. An apparatus for predicting intake manifoldpressure according to claim 10, wherein in the fuzzy estimator a secondorder difference of values of intake manifold pressure is further usedas another input and fuzzy rules are determined based on an amount ofthe difference of values of intake manifold pressure, an amount of thedifference of values of throttle opening and an amount of the secondorder difference of values of intake manifold pressure.
 13. An apparatusfor predicting intake manifold pressure according to claim 12, whereinin the fuzzy estimator a second order difference of values of intakemanifold pressure is classified based on its amount into positive one,that of zero or negative one and fuzzy rules are provided respectivelyfor areas determined by three kinds of classifications based on anamount of the difference of values of intake manifold pressure, anamount of the difference of values of throttle opening and an amount ofthe second order difference of values of intake manifold pressure. 14.An apparatus for predicting intake manifold pressure according to claim10, wherein the apparatus further comprises a module for delaying aninput throttle opening value by a time delay, and the delayed value isused as an output throttle opening value.
 15. An apparatus forpredicting intake manifold pressure according to claim 10, wherein arelationship between an input throttle opening value and a desired valueof throttle opening is modeled using a time delay element and a lagsystem and a value estimated through the model and the desired value isused as an output throttle opening value.
 16. An apparatus forpredicting intake manifold pressure according to claim 10, wherein amembership function for the consequent part of the estimating with fuzzyreasoning comprises a bar-shaped singleton function.
 17. An apparatusfor predicting intake manifold pressure according to claim 10, furthercomprising a filter for filtering an input.
 18. An apparatus forpredicting intake manifold pressure according to claim 17, wherein thefilter comprises an adaptive filter.
 19. A computer-readable mediumhaving a program stored therein, the program is made to perform thesteps of: obtaining a difference of values of intake manifold pressureand a difference of values of throttle opening; obtaining a predicteddifference of values of intake manifold pressure by estimating withfuzzy reasoning, including fuzzy rules determined based on an amount ofthe difference of values of intake manifold pressure and an amount ofthe difference of values of throttle opening; and adding the predicteddifference of values of intake manifold pressure, to a value of intakemanifold pressure, to obtain a predicted value of intake manifoldpressure.
 20. A computer-readable medium according to claim 19, whereinthe difference of values of intake manifold pressure is classifiedaccording to a first classification based on its amount into positiveone, that of zero or negative one and the difference of values ofthrottle opening is classified according to a second classificationbased on its amount into positive one, that of zero or negative one andfuzzy rules are provided respectively for areas determined by the firstclassification and the second classification.
 21. A computer-readablemedium according to claim 19, wherein in the step of obtaining adifference, a second order difference of values of intake manifoldpressure, is further obtained and in the step of obtaining the predicteddifference, fuzzy rules are determined based on an amount of thedifference of values of intake manifold pressure, an amount of thedifference of values of throttle opening and an amount of the secondorder difference of values of intake manifold pressure.
 22. Acomputer-readable medium according to claim 21, wherein a second orderdifference of values of intake manifold pressure is classified based onits amount into positive one, that of zero or negative one and fuzzyrules are provided respectively for areas determined by three kinds ofclassifications based on an amount of the difference of values of intakemanifold pressure, an amount of the difference of values of throttleopening and an amount of the second order difference of values of intakemanifold pressure.
 23. A computer-readable medium according to claim 19,wherein a value obtained by delaying an input throttle opening value bya time delay, is used as an output throttle opening value.
 24. Acomputer-readable medium according to claim 19, wherein a relationshipbetween an input throttle opening value and a desired value of throttleopening is modeled using a time delay element and a lag system and avalue estimated through the model and the desired value is used as anoutput throttle opening value.
 25. A computer-readable medium accordingto claim 19, wherein the membership function for the consequent part ofthe estimating with fuzzy reasoning comprises a bar-shaped singletonfunction.
 26. A computer-readable medium according to claim 19, whereinthe program further comprises the step of filtering between the step ofobtaining differences and the step of obtaining a predicted difference.27. A computer-readable medium according to claim 26, wherein thefiltering is carried out with an adaptive filter.
 28. An apparatus forpredicting intake manifold pressure, said apparatus comprising: meansfor obtaining a difference of values of intake manifold pressure; meansfor obtaining a difference of values of throttle opening; and fuzzyestimator means for receiving as inputs the difference of values ofintake manifold pressure and the difference of values of throttleopening and obtaining and outputting a predicted difference of values ofintake manifold pressure by estimating with fuzzy reasoning, includingfuzzy rules determined based on an amount of the difference of values ofintake manifold pressure and an amount of the difference of values ofthrottle opening.
 29. An apparatus for predicting intake manifoldpressure according to claim 28, wherein in the fuzzy estimator means thedifference of values of intake manifold pressure is classified accordingto a first classification based on its amount into positive one, that ofzero or negative one and the difference of values of throttle opening isclassified according to a second classification based on its amount intopositive one, that of zero or negative one and fuzzy rules are providedrespectively for areas determined by the first classification and thesecond classification.
 30. An apparatus for predicting intake manifoldpressure according to claim 28, wherein in the fuzzy estimator means asecond order difference of values of intake manifold pressure is furtherused as another input and fuzzy rules are determined based on an amountof the difference of values of intake manifold pressure, an amount ofthe difference of values of throttle opening and an amount of the secondorder difference of values of intake manifold pressure.
 31. An apparatusfor predicting intake manifold pressure according to claim 30, whereinin the fuzzy estimator means a second order difference of values ofintake manifold pressure is classified based on its amount into positiveone, that of zero or negative one and fuzzy rules are providedrespectively for areas determined by three kinds of classificationsbased on an amount of the difference of values of intake manifoldpressure, an amount of the difference of values of throttle opening andan amount of the second order difference of values of intake manifoldpressure.
 32. An apparatus for predicting intake manifold pressureaccording to claim 28, wherein the apparatus further comprises modulemeans for delaying an input throttle opening value by a time delay, andthe delayed value is used as an output throttle opening value.
 33. Anapparatus for predicting intake manifold pressure according to claim 28,wherein a relationship between an input throttle opening value and adesired value of throttle opening is modeled using a time delay elementand a lag system and a value estimated through the model and the desiredvalue is used as an output throttle opening value.
 34. An apparatus forpredicting intake manifold pressure according to claim 28, wherein themembership function for the consequent part of the estimating with fuzzyreasoning comprises a bar-shaped singleton function.
 35. An apparatusfor predicting intake manifold pressure according to claim 28, furthercomprising filter means for filtering an input.
 36. An apparatus forpredicting intake manifold pressure according to claim 35, wherein thefilter means comprises an adaptive filter.